[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2315":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":68},2315,"A practical workflow combining Kaplan-Meier and Bayesian accelerated failure time analyses for censored reproductive phenology data in soybean","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-09851-6","Abstract Developmental progression in plants is inherently a time-to-event process, yet plant phenology data are often analyzed as ordinary endpoint traits even when some individuals fail to reach the target stage within the observation window. Such observations are right-censored rather than missing and should be retained in inference. Here, we evaluated 21 soybean cultivars across 10 controlled-environment treatment combinations varying in CO 2 concentration, photoperiod, and LED light quality, and analyzed days from sowing to the R6 stage using a practical workflow that combined Kaplan-Meier survival analysis with a Bayesian Weibull accelerated failure time (AFT) model. Based on 210 cultivar × treatment observations derived from 1,050 plants, treatments resolved into favorable, intermediate, and strongly inhibitory classes. Elevated CO 2 showed the strongest association with reproductive timing: increasing CO 2 from ambient to 1000–1400 ppm reduced median survival time, while the additional gain from 1000 ppm to 1400 ppm was minimal, indicating a saturating response. Blue - Red spectral treatments and 6–8 h photoperiods were associated with rapid and synchronized development within the tested treatment combinations. The Bayesian AFT model included treatment as a random effect to account for the chamber-level experimental structure; the between-treatment standard deviation was estimated at 0.02 (95% credible interval: 0.00–0.06), indicating negligible chamber-to-chamber variation. The highest-ranked predicted combination (1400 ppm, blue: red = 2:1, 6 h) gave a median time to R6 of 66.90 d (mean 72.18 d). This study provides a practical and transferable workflow for censored plant phenology datasets in controlled-environment research, phenotyping, and breeding. The workflow integrates established survival analysis methods and explicitly accounts for chamber-level design, offering a framework for structured plant phenotyping experiments.","摘要 植物的发育进程本质上是一个时间到事件的过程，然而植物物候数据常常被当作普通的终点性状来分析，即使部分个体在观测窗口内未能达到目标阶段也是如此。此类观测属于右删失而非缺失，应在推断中予以保留。本研究评估了21个大豆品种在10种控制环境处理组合下的表现，这些组合在CO₂浓度、光周期和LED光质上有所不同，并采用将Kaplan-Meier生存分析与贝叶斯Weibull加速失效时间（AFT）模型相结合的实际工作流程，分析了从播种到R6阶段的天数。基于来自1，050株植物的210个品种×处理观测值，各处理可归纳为有利、中等和强抑制三类。升高CO₂与生殖时序的关联最强：将CO₂从环境浓度提高至1000–1400 ppm可缩短中位生存时间，而从1000 ppm进一步提高至1400 ppm的额外增益极小，表明存在饱和响应。在测试的处理组合中，蓝红光光谱处理和6–8 h光周期与快速且同步的发育相关。贝叶斯AFT模型将处理作为随机效应纳入，以考虑 chamber 层面的实验结构；处理间标准差估计为0.02（95%可信区间：0.00–0.06），表明 chamber 间变异可忽略不计。排名最高的预测组合（1400 ppm，蓝：红=2：1，6 h）给出的R6中位时间为66.90 d（均值72.18 d）。本研究为控制环境研究、表型分析和育种中的删失植物物候数据集提供了一个实用且可迁移的工作流程。该工作流程整合了已有的生存分析方法，并明确考虑了 chamber 层面的设计，为结构化植物表型实验提供了一个框架。",null,"BMC Plant Biology","2026-09-12T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,14,9,1,"提出结合Kaplan-Meier与贝叶斯AFT模型的删失表型数据分析流程，方法可迁移，对大豆生殖物候与受控环境育种表型研究有实用价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","设施农业","育种","大豆","表型组学",0,"10.1186\u002Fs12870-026-09851-6",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":61,"direction":65,"ingested_from":67},"W7212354617",[37,40,42,45,47,49,51,53,55,57,59],{"name":38,"orcid":39},"Fan Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-8576-0273",{"name":41,"orcid":9},"Xiao Cui",{"name":43,"orcid":44},"Kanglin Liu","https:\u002F\u002Forcid.org\u002F0000-0001-8747-5022",{"name":46,"orcid":9},"Huilong Hong",{"name":48,"orcid":9},"Xin Su",{"name":50,"orcid":9},"Yajun Xiong",{"name":52,"orcid":9},"Sawaira Jadoon",{"name":54,"orcid":9},"Huan Yu",{"name":56,"orcid":9},"Yijie Chen",{"name":58,"orcid":9},"Qiu Lijuan",{"name":60,"orcid":9},"Jun Wang",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"用Kaplan-Meier与贝叶斯AFT分析大豆R6期右删失数据，评估21个品种在10种环境组合下的","21个大豆品种、1050株、10种CO2\u002F光周期\u002FLED光质组合，Kaplan-","CO2升高缩短R6中位时间但1000-1400ppm增益极小，蓝红2:1与6-8h光周期发育最快， ","农业遥感与作物表型","可将该删失表型分析流程推广至田间多环境育种数据，并耦合高通量表型图像自动判定发育阶段。","openalex","2026-09-13T23:30:19.792952Z"]